Color Image Quality Assessment Based on VIF

Kuo, Tien-Ying; Wei, Yu-Jen; Wan, Kuan-Hung

doi:10.1109/icispc.2019.8935651

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…VIF models the quality assessment problem as an information fidelity criterion, quantifying the mutual information between the reference image and the distorted image relative to the image information extracted by HVS. The VIF metric [ 18 ] can be expressed as

where E is the image perceived by the HVS. I ( C ; F ) and I ( C ; E ) represent the information that the brain can extract in reference and test images, respectively.…”

Section: Results Analysismentioning

confidence: 99%

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An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

Yue

Chunqing

Yuehui

2022

Computational Intelligence and Neuroscience

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The role of medical image technology in the medical field is becoming more and more obvious. Doctors can use medical image technology to more accurately understand the patient’s condition and make accurate judgments and diagnosis and treatment in order to make a large number of medical blurred images clear and easy to identify. Inspired by the human vision system (HVS), we propose a simple and effective method of low-light image enhancement. In the proposed method, first a sampler is used to get the optimal exposure ratio for the camera response model. Then, a generator is used to synthesize dual-exposure images that are well exposed in the regions where the original image is underexposed. Next, the enhanced image is processed by using a part of low-light image enhancement via the illumination map estimation (LIME) algorithm, and the weight matrix of the two images will be determined when fusing. After that, the combiner is used to get the synthesized image with all pixels well exposed, and finally, a postprocessing part is added to make the output image perform better. In the postprocessing part, the best gray range of the image is adjusted, and the image is denoised and recomposed by using the block machine 3-dimensional (BM3D) model. Experiment results show that the proposed method can enhance low-light images with less visual information distortions when compared with those of several recent effective methods. When it is applied in the field of medical images, it is convenient for medical workers to accurately grasp the details and characteristics of medical images and help medical workers analyze and judge the images more conveniently.

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where E is the image perceived by the HVS. I ( C ; F ) and I ( C ; E ) represent the information that the brain can extract in reference and test images, respectively.…”

Section: Results Analysismentioning

confidence: 99%

Section: Visual Information Fidelity (Vif)mentioning

confidence: 99%

An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

Yue

Chunqing

Yuehui

2022

Computational Intelligence and Neuroscience

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Infrared and visible fusion imaging via double-layer fusion denoising neural network

Cheng

et al. 2022

Digital Signal Processing

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An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

Radhabai,

KVN,

Shanmugam

et al. 2024

BMC Med Imaging

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As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.

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Color Image Quality Assessment Based on VIF

Cited by 3 publications

References 7 publications

An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

Infrared and visible fusion imaging via double-layer fusion denoising neural network

An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

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